/*
 *   This program is free software: you can redistribute it and/or modify
 *   it under the terms of the GNU General Public License as published by
 *   the Free Software Foundation, either version 3 of the License, or
 *   (at your option) any later version.
 *
 *   This program is distributed in the hope that it will be useful,
 *   but WITHOUT ANY WARRANTY; without even the implied warranty of
 *   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *   GNU General Public License for more details.
 *
 *   You should have received a copy of the GNU General Public License
 *   along with this program.  If not, see <http://www.gnu.org/licenses/>.
 */

/*
 *    Classifier.java
 *    Copyright (C) 1999-2012 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.classifiers;

import weka.core.Capabilities;
import weka.core.Instance;
import weka.core.Instances;

/**
 * Classifier interface. All schemes for numeric or nominal prediction in Weka
 * implement this interface. Note that a classifier MUST either implement
 * distributionForInstance() or classifyInstance().
 *
 * @author Eibe Frank (eibe@cs.waikato.ac.nz)
 * @author Len Trigg (trigg@cs.waikato.ac.nz)
 * @version $Revision$
 */
public interface Classifier {

    /**
     * Generates a classifier. Must initialize all fields of the classifier that are
     * not being set via options (ie. multiple calls of buildClassifier must always
     * lead to the same result). Must not change the dataset in any way.
     *
     * @param data set of instances serving as training data
     * @exception Exception if the classifier has not been generated successfully
     */
    public abstract void buildClassifier(Instances data) throws Exception;

    /**
     * Classifies the given test instance. The instance has to belong to a dataset
     * when it's being classified. Note that a classifier MUST implement either this
     * or distributionForInstance().
     *
     * @param instance the instance to be classified
     * @return the predicted most likely class for the instance or
     *         Utils.missingValue() if no prediction is made
     * @exception Exception if an error occurred during the prediction
     */
    public double classifyInstance(Instance instance) throws Exception;

    /**
     * Predicts the class memberships for a given instance. If an instance is
     * unclassified, the returned array elements must be all zero. If the class is
     * numeric, the array must consist of only one element, which contains the
     * predicted value. Note that a classifier MUST implement either this or
     * classifyInstance().
     *
     * @param instance the instance to be classified
     * @return an array containing the estimated membership probabilities of the
     *         test instance in each class or the numeric prediction
     * @exception Exception if distribution could not be computed successfully
     */
    public double[] distributionForInstance(Instance instance) throws Exception;

    /**
     * Returns the Capabilities of this classifier. Maximally permissive
     * capabilities are allowed by default. Derived classifiers should override this
     * method and first disable all capabilities and then enable just those
     * capabilities that make sense for the scheme.
     *
     * @return the capabilities of this object
     * @see Capabilities
     */
    public Capabilities getCapabilities();
}
